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SeekJudge: A Practical Reward Framework for Reinforcement Learning in Computer-Use Agents
Deciding whether a trajectory actually fulfills its instruction determines how we measure computer-use agents on long-horizon graphical-user-interface tasks and how we train them with reinforcement learning (RL). This judgment has long relied on rule-based evaluation. Rule-based evaluation often dis
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